My journey into education research began with questions about how anatomy and physiology (A&P) courses could better engage students and support lasting learning. What started as curiosity about teaching methods and student outcomes grew into a sustained line of inquiry that underpins my approach to instruction and defining “what works” in higher education.

• Anatomy and Physiology beyond “this is interesting:” Assessing transformative experiences through Epistemic Network Analysis. Journal of Science Education and Technology, 2025. Read article »

• A scoping review of undergraduate anatomy and physiology education: Approaches to evaluating student outcomes in the United States. Journal of Microbiology & Biology Education, 2024, 25(2). Read article »

• Effect of quiz format on student performance and answer-changing behaviour on formative assessments. Journal of Biological Education, 2019. Read article »

Questions about how students see themselves as “science people” have been central to my research on science identity. This work explores how identity shapes persistence, engagement, and achievement. These studies inform my teaching by highlighting the importance of belonging, self-efficacy, and relevance in science education.

• The anatomy of persistence: Remediation and science identity perceptions in undergraduate anatomy and physiology. International Journal of Higher Education, 2020, 9(5), 283–299. Read article »

• Are there any science people in undergraduate health science courses? Assessing science identity among pre-nursing and pre-allied health students in a community college setting. Journal of Research in Science Teaching, 2023, 1–35. Read article »

During my postdoctoral research, I explored what it takes to design and validate a geoscience concept inventory that can be assessed using machine learning. This work focused on evaluating how students connect complex, interdisciplinary concepts and developing automated methods to classify responses by expertise level. This project combines disciplinary education research with innovations in natural language processing to improve large-scale assessment in science education.

• FEW questions, many answers: Using machine learning to assess how students connect food–energy–water (FEW) concepts. Humanities & Social Sciences Communications, 2024, 11(1), 1033–18. Read article »

• Extending a pretrained language model (BERT) using an ontological perspective to classify students’ scientific expertise level from written responses. In review. Read preprint »